Senior Data Scientist

The Senior Data Scientist provides advanced analytical, machine learning, and data engineering support. This role combines deep expertise in predictive modeling, statistical analysis, and scalable data pipeline development to deliver production-ready solutions that drive data-informed decision-making and operational efficiency.

Duties include:

Design, develop, and deploy machine learning models for classification, regression, time series forecasting, and natural language processing applications to solve complex business problems.

• Build and optimize automated, scalable ETL/ELT pipelines using Python, SQL, and cloud-based tools to integrate, transform, and validate structured and unstructured data from diverse sources.

• Develop and maintain production ML systems including model deployment, monitoring, versioning, and performance tracking in collaboration with AI/ML infrastructure teams.

• Design, develop, and deploy interactive dashboards and data visualizations using Tableau, Power BI, or similar platforms to deliver actionable insights to technical and executive stakeholders.

• Perform end-to-end model development including exploratory data analysis, feature engineering, hyperparameter tuning, model validation, and documentation.

• Develop and maintain data pipelines and workflows using tools such as AWS services, Databricks, and GitLab CI/CD to support analytics and ML operations.

• Conduct data mining, cleaning, and manipulation using SQL, Python (Pandas, NumPy), or R to deliver statistical analyses, visualizations, and predictive insights.

• Implement data quality and validation frameworks, leveraging APIs and automated testing to ensure accuracy and completeness across systems.

• Translate complex business requirements into technical solutions, data models, and analytical frameworks that align with long-term technology strategy.

• Provide technical mentorship to team members on advanced analytics techniques, Python scripting, ML best practices, and workflow automation.

• Create comprehensive documentation including data dictionaries, metadata, technical specifications, and presentations for diverse audiences.

• Respond to urgent and ad-hoc data requests, compile reports for leadership, and coordinate collaborative research and analysis projects.

• Partner with cross-functional teams including data engineers, software developers, and federal stakeholders to ensure production readiness and scalability of data solutions.

This position is fully remote and requires a Public Trust (or the ability to obtain it). The candidate may be required to work outside of business hours including weekends based on need.

Education:

Requires BS/BA degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field. Master's degree preferred.

Qualifications Required:

• At least 5 years of experience in data science, machine learning, or advanced analytics; 3 years of experience with a Master's degree.

• Demonstrated experience developing and deploying machine learning models using Python libraries (Scikit-learn, XGBoost, TensorFlow, PyTorch, or HuggingFace).

• Strong proficiency in Python and SQL for data manipulation, analysis, and pipeline development.

• Experience with ETL/ELT pipeline development and data engineering best practices.

• Demonstrated knowledge of data visualization platforms (Tableau, Power BI) and ability to translate technical insights into executive-level dashboards.

• Experience with cloud platforms (AWS, Databricks) and modern data infrastructure.

• Knowledge of statistical analysis and modeling techniques.

• Understanding of relational and non-relational databases (Oracle SQL, PostgreSQL, etc.).

• Strong version control and collaboration skills using Git (GitHub, GitLab, BitBucket).

• Exceptional analytical skills with strong attention to detail.

• Strong written and verbal communication skills with ability to present complex findings to non-technical stakeholders.

• Must be able to work both independently and as part of a collaborative team in a fast-paced, agile environment.

Preferred:

• Experience with MLOps practices including model monitoring, versioning, and production deployment.

• Experience with CI/CD pipelines (GitLab CI/CD) for data workflows and ML operations.

• Knowledge of time series forecasting, natural language processing, or geospatial analysis, generative AI, or agentic AI.

• Experience with data orchestration and workflow automation tools.

• Familiarity with feature engineering, dimensionality reduction (PCA), cluster analysis, and anomaly detection techniques.

• Experience working with federal government data systems and compliance requirements.

• Background in Agile/Scrum methodologies and project management tools (Jira).

• Experience mentoring junior data professionals and establishing analytics best practices.